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Research Paper|Volume 15, Issue 24|pp 15340—15359

Analysis of cancer-associated fibroblasts in cervical cancer by single-cell RNA sequencing

Shuang Wen1, Xuefeng Lv2, Pengxiang Li3, Jinpeng Li2, Dongchun Qin4
  • 1Reproductive Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
  • 2Department of Laboratory Medicine, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
  • 3Henan Provincial Chest Hospital, Zhengzhou, Henan, China
  • 4Department of Laboratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
* Equal contribution and co-first author
Received: July 10, 2023Accepted: November 10, 2023Published: December 28, 2023
https://doi.org/10.18632/aging.205353

Copyright: © 2023 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objective: Since scRNA-seq is an effective tool to study tumor heterogeneity, this paper intends to reveal the differences of cervical cancer in patients at the individual cell level by scRNA-seq, and focus on the biological functions of cancer-associated fibroblasts (CAFs) in cervical cancer, facilitating the provision of a new interpretation of the heterogeneity of the microenvironment of cervical cancer, and an in-depth exploration of the pathogenesis of cervical cancer as well as pursuit of effective means of treatment intake.

Methods: 3 cervical cancer specimens were collected by clinical surgery for single-cell RNA sequencing. Cell suspensions of fresh cervical cancer tissues were prepared, and cDNA libraries were created and sequenced on the machine. Furthermore, the sequencing data were analyzed using bioinformatics, including descending clustering of cells, identification of cell populations, mimetic time series analysis, inferCNV, cell communication analysis, and identification of transcription factors.

Results: A total of 9 cell types were identified, encompassing T cells, epithelial cells, smooth muscle cells, CAFs, endothelial cells, macrophages, B cells, lymphocytes, and plasma cells. CAFs were further divided into three cell subtypes, named type1 cells, type2 cells, and type3 cells. With key transcription factors for the three cells, TCF21, ZC3H11A, and MYEF2 obtained, this research revealed the communication relationship between CAFs and several other cells, and found an important role of CAFs in the MK signaling pathway.

Conclusions: scRNA-seq technology contributed to exploring the tumor heterogeneity of cervical cancer more deeply, and also further gaining insight into the biological functions of CAFs in cervical cancer.